The soybean in China northeastern black soil has low yield, low use efficiency of water and fertilizer, which has overly high amount of nitrogen residual. Three different drip irrigation mode amount (300 mm, 350 mm, and 400 mm) and five different amount of nitrogen was designed in this paper to study the influence of water and nitrogen controlling to the water and fertilizer use efficiency and the amount of nitrogen residual under drip irrigation mode. According to our experiment results, the water consumption of soybean increased with the growth of drip irrigation mode amount, and in all treatment, soybean consumed the most amount of water at seed filling period. There is no significant difference in water consumption amount between each treatment during seedling period, but there were significant differences in the water consumption of soybean at branching period, flowering and pod period, and seed filling period. Nitrogen fertilizer alone does not have significant influence on soybean water, but the interaction between nitrogen fertilizer and soybean had significant difference in water consumption amount. The yield reached the biggest amount of 3250 kg/hm(2) when nitrogen drip amount was 400 mm, and nitrogen application rate was 81 kg/hm(2). The yield increased with the growth of irrigation amount, but the yield increase rate became slow. When the nitrogen application amount was at a low level, there was a positive interaction between water and nitrogen, which showed a synergistic acceleration effect. When the nitrogen application rate was at a high level, there was a negative interaction between water and nitrogen. When the drip irrigation mode amount was 350 mm and the nitrogen application rate was 81 kg/hm(2), the WUE was the highest, which was 0.60 kg/m(3). In a certain range of nitrogen application, increasing irrigation amount can promote WUE, while the amount was over a certain range, WUE decreased. After harvest, the amount of nitrate nitrogen residual in the 0-100 cm soil varied greatly among different nitrogen fertilizer treatments, and the nitrogen fertilizer application amount was greater, the nitrogen residual was greater. In the W3N3 treatment which has the highest yield, the yield was at 42 kg/hm(2) less than the W3N4 treatment at the highest amount of nitrate nitrogen residual, which is a reduction of 35.78%. The results showed that proper combination of water and nitrogen application could reduce nitrogen residual in soil after harvest. The research results can provide theoretical basis and technical support for the efficient utilization of water and fertilizer resources in the black soil region located in northeastern China.

Purpose/Objective(s) For esophageal squamous cell carcinomas (ESCC) patients received concurrent chemo-radiotherapy (CCRT), local recurrence is the most common failure pattern and reliable markers for prognosis are lacking. Previous studies have demonstrated the predictive role of traditional radiomics features for prediction of local recurrence-free survival (LRFS) in ESCC. Nevertheless, traditional radiomics based on human-defined handcrafted features may not fully characterize tumor heterogeneity. Some studies have provided evidence that deep learning with advantages in voxel analysis could provide remarkable performance. This study aims to establish and validate a deep learning model for predicting LRFS in ESCC patients received CCRT. Materials/Methods We retrospectively included 302 patients from Xijing Hospital and randomly divided them into training set (201) and internal validation set (101) according to 2:1. 95 patients from Tianjin Cancer Hospital and Shandong Cancer Hospital were included as the external validation set. All patients underwent a contrast-enhanced computed tomography (CE-CT) scan before CCRT and were followed up for more than 24 months after CCRT. The deep learning model was developed by using 3D-Densenet deep learning architecture. Manually segmented tumors based on CE-CT were used as model input, LRFS was the prediction target. The deep learning signature was built using the deep-score output by the model. Results The median follow-up time of all patients was 26.77 months and 257 of 397 patients (64.74%) were confirmed local recurrence or death during the follow-up period. The deep learning model for prediction of LRFS in ESCC patients received CCRT showed good prognostic performance, with a C-index of 0.7337 (95% CI: 0.6800–0.7874) in the training set, which was validated in the internal (0.7203 [95% CI: 0.6450–0.7957]) and external validation (0.7167 [95% CI: 0.6416–0.7918]) sets, respectively. Kaplan-Meier survival analysis showed that the median of deep-score (-0.06) could stratify patients into high and low-risk groups for different LRFS. The low-risk group with the lower deep-score had a significantly higher LRFS than that of the high-risk group with a high deep-score (2-year LRFS 71.1% vs 33.0%, p<0.0001) in the training set. The result was validated in the internal (2-year LRFS 58.8% vs34.8%, p<0.01) and external validation (2-year LRFS 61.9% vs 22.4%, p<0.0001) sets, respectively. Conclusion Deep learning signature can be used as a non-invasive radiomics marker to predict LRFS in ESCC patients received CCRT. This is the first multicenter-based study using deep learning to predict local recurrence in esophageal cancer.

The objective of this study was to analyse the black soil rice fields of the northeastern cold region of China, D311 optimal design scheme with three factors secondary saturation was adopted and static opaque chamber - gas chromatographic method was utilized to analyze the effect of irrigation amount, nitrogen fertilizer and straw biochar on the emission of the greenhouse gas CH4 from rice fields, the study determined the optimal application scheme of water and fertilizer for emission control. The results show that the order of influence for these factors from the highest to the lowest is: biochar > nitrogen fertilizer > water; effect of irrigation amount on CH4 emission is increased at first, followed by a decrease. Increase of nitrogen fertilizer and biochar can significantly reduce CH4 emission loads; interaction between two factors has an inhibitory effect on CH4 emissions and it is shown as below: nitrogen fertilizer + biochar > water + biochar > water + nitrogen fertilizer; in combination with the yield, when emission reduction target of rice field CH4 is controlled at 20 similar to 40% of normal emission, the optimized application scheme in combination of water, fertilizer and biochar is the following: irrigation amount 4,930-5,310 m(3)/hm(2), nitrogen application amount 96.93-107.74 kg/hill' and biochar application amount 19.71-24.12 t/hm(2).

Accumulating evidence indicates that thrombin, the major effector of the coagulation cascade, plays an important role in the pathogenesis of asthma. Interestingly, dabigatran, a drug used in clinical anticoagulation, directly inhibits thrombin activity. The aim of this study was to investigate the effects and mechanisms of dabigatran on airway smooth muscle remodeling in vivo and in vitro. Here, we found that dabigatran attenuated inflammatory pathology, mucus production, and collagen deposition in the lungs of asthmatic mice. Additionally, dabigatran suppressed Yes‐associated protein (YAP) activation in airway smooth muscle of asthmatic mice. In human airway smooth muscle cells (HASMCs), dabigatran not only alleviated thrombin‐induced proliferation, migration and up‐regulation of collagen I, α‐SMA, CTGF and cyclin D1, but also inhibited thrombin‐induced YAP activation, while YAP activation mediated thrombin‐induced HASMCs remodeling. Mechanistically, thrombin promoted actin stress fibre polymerization through the PAR1/RhoA/ROCK/MLC2 axis to activate YAP and then interacted with SMAD2 in the nucleus to induce downstream target genes, ultimately aggravating HASMCs remodeling. Our study provides experimental evidence that dabigatran ameliorates airway smooth muscle remodeling in asthma by inhibiting YAP signalling, and dabigatran may have therapeutic potential for the treatment of asthma.

Background: Aberrant Wnt/beta-catenin signaling is a well-established characteristic of breast cancer implicated in tumorigenesis and metastasis, whereas platelet-activating factor acetylhydrolase (PLA2G7/PAFAH) is not well understood in breast carcinogenesis so far. This study analyzes the functional relationship between PAFAH and beta-catenin in breast cancer tissue.

Local recurrence after was the main pattern of failure for esophageal squamous cell carcinoma (ESCC) receiving definitive chemoradiotherapy. There is no ideal clinical features or blood biomarkers available to predict local recurrence of ESCC. Several studies have used CT radiomics to predict survival and treatment response of ESCC receiving chemoradiotherapy. However, little is known about the role of CT radiomics in prediction of local recurrence. We intended to establish and validate a pretreatment CT radiomics nomogram for prediction of local recurrence-free survival (LRFS) in ESCC patients receiving definite chemoradiotherapy. Three hundred eleven ESCC patients receiving IMRT or VMAT based definite chemoradiotherapy (232 in training and 79 in validation cohort) between February 2009 and June 2015 were enrolled. 2338 radiomics features were extracted from pretreatment contrast-enhanced computed tomography (CT) images of the primary tumor volume. Least absolute shrinkage and selection operator (LASSO) regression was used to select features for the radiomics signature. A radiomics nomogram was established by integrating the clinical data and radiomics signature. Nomogram discrimination and calibration were evaluated. The radiomics signature consisting of 4 selected features from contrast-enhanced CT images showed good prognostic performance in terms of predicting LRFS in two cohorts. Radiomics signature could stratify patients into high and low-risk group with different prognosis. Low-risk groups defined by radiomics signature (radscore cutoff value: -0.1152) had significant better LRFS than high-risk groups in training (p<0.001; 2-y LRFS 87.5% vs 46.6%; 3-y LRFS 82.5% vs 45.74%) and validation cohorts (p<0.05; 2-y LRFS 73.1% vs 54.4%; 3-y LRFS 73.1% vs 50.5%), respectively. Similarly, survival analysis also showed Low-risk groups defined by the same radiomics signature had significant better OS than high-risk groups in training (p<0.001; 2-y OS 69.3% vs 42%; 3-y OS 57.8% vs 35.6%) and validation cohorts (p<0.05; 2-y OS 51.9% vs 48.1%; 3-y OS 51.9% vs 33.4%), respectively. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.68vs 0.624; P< 0.01). Calibration curves showed good agreement. The most optimal predictive power for evaluating LRFS could be achieved when radiomics signature was added to the clinical data. Pretreatment contrast-enhanced CT radiomics could be helpful for personalized risk stratification and treatment in ESCC patients receiving definitive chemoradiotherapy.

Background: The classical diagnostic method for the prognosis prediction of gastric cancer is imprecise. Thus, tools that can precisely predict the prognosis of gastric cancer subtypes and guide treatment strategies are needed.Methods: Using publicly available data from The Cancer Genome Atlas and Gene Expression Omnibus, we established tumor-noncoding RNA-microenvironment type (TNM) scores to define a combination of signatures that reflect the expression levels of tumor-related protein-coding genes (tumor score), tumor-related noncoding genes (NC score), and the cellular and stromal composition of the tumor microenvironment (TME score). These signatures were used to predict the prognosis of gastric cancer patients and the therapeutic effect of immunotherapy on gastric cancer.Results: Based on TNM scores, gastric cancer patients were divided into three subgroups and Kaplan-Meier survival curves revealed significant differences among the subgroups (P < 0.001). Tumor score was positively associated with the epithelial-mesenchymal transition signaling pathway and transforming growth factor-beta signaling pathway and negatively associated with DNA damage repair. The TME score showed opposite results and was also positively associated with cytotoxic T-cells and immune check-points. We also explored the predictive ability of TNM scores for the effectiveness of anti-PD-1/PD-L1 treatment. Receiver operating characteristic curves showed that a combination of TME score, tumor score, and tumor mutation burden could predict the treatment effect well (area under the curve = 0.708).Conclusion: Our study showed that the TNM scores could be used as a robust predicting tool for the prognosis of gastric cancer and the treatment effects of check-point inhibitors.Funding: This study was supported by the National Natural Science Foundation of China (No. 81772580, and 81811530024 to Wangjun Liao); Guangzhou Planed Project of Science and Technology (No. 201803010070 to Wangjun Liao).Declaration of Interest: The authors declare that they have no competing interests.

Most of the previous studies using various scoring systems have shown that SLC is negatively correlated with final procedural success of PCI for CTO (PCI-CTO). However, development of new guidewires and use of SLC as a navigation aid may have increased the CTO crossing rate. We hypothesized that SLC